Chao Wang
ABSTRACT
To discover relationships and associations between pairs of variables in large data sets have become one of the most significant challenges for bioinformatics scientists. To tackle this problem, maximal information coefficient (MIC) is widely applied as a measure of the linear or non-linear association between two variables. To improve the performance of MIC calculation, in this work we present MIC++, a parallel approach based on the heterogeneous accelerators including Graphic Processing Unit (GPU) and Field Programmable Gate Array (FPGA) engines, focusing on both coarse-grained and fine-grained parallelism. As the evaluation of MIC++, we have demonstrated the performance on the state-of-the-art GPU accelerators and the FPGA-based accelerators. Preliminary estimated results show that the proposed parallel implementation can significantly achieve more than 6X-14X speedup using GPU, and 4X-13X using FPGA-based accelerators.
- Karpinets T.V., et al., Analyzing large biological datasets with association networks. Nucleic Acids Res, 2012. 40(17):e131.Google Scholar
Cross Ref
- Reshef, D.N., et al., Detecting Novel Associations in Large Data Sets. Science, 2011. 334: p. 1518--1524.Google Scholar
- Tang, D., et al., RapidMic: Rapid Computation of the Maximal Information Coefficient. Evolutionary Bioinformatics, 2014.Google Scholar
- Wang, C., et al., Accelerating Computation of Large Biological Datasets using MapReduce Framework, IEEE/ACM Trans. on Computational Biology and Bioinformatics, 2016.Google Scholar
Index Terms
- Brief Announcement: MIC++: Accelerating Maximal Information Coefficient Calculation with GPUs and FPGAs
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